# Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates

arXiv: https://arxiv.org/abs/2407.11180 · HTML: https://mlsanity.com/paper/2407.11180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 22 of 22)

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- [MultiFun-DAG: Multivariate Functional Directed Acyclic Graph](https://mlsanity.com/paper/2404.13836/) (2024)

## Abstract (arXiv)

The steam drum water level is a critical parameter that directly impacts the safety and efficiency of power plant operations. However, predicting the drum water level in boilers is challenging due to complex non-linear process dynamics originating from long-time delays and interrelations, as well as measurement noise. This paper investigates the application of Transformer-based models for predicting drum water levels in a steam boiler plant. Leveraging the capabilities of Transformer architectures, this study aims to develop an accurate and robust predictive framework to anticipate water level fluctuations and facilitate proactive control strategies. To this end, a prudent pipeline is proposed, including 1) data preprocess, 2) causal relation analysis, 3) delay inference, 4) variable augmentation, and 5) prediction. Through extensive experimentation and analysis, the effectiveness of Transformer-based approaches in steam drum water level prediction is evaluated, highlighting their potential to enhance operational stability and optimize plant performance.